Abstract: This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and semantic dependencies”. Both algorithms start parsing the sentence using the same syntactic parser. The first algorithm uses machine learning methods to identify the semantic dependencies in four stages: identification and labeling of predicates, identification and labeling of arguments. The second algorithm uses a generative probabilistic model, choosing the semantic dependencies that maximize the probability with respect to the model. A hybrid algorithm combining the best stages of the two algorithms attains 86.62% labeled syntactic attachment accuracy, 73.24% labeled semantic dependency F1 and 79.93% labeled macro F1 score for the combined WSJ and Brown test sets.

August 01, 2008

Current position: Forward Deployed Engineer at Palantir Technologies, New York office. (email, website, linkedin). M.S. Thesis: Evolution of Compositionality with a Bag of Words Syntax. Koç University Department of Computer Engineering, August 2008. (Download PDF).Abstract:In the last two decades, the idea of an emerging and evolving language has been studied thoroughly. The main question behind this kind of studies is how a group of humans reaches an agreement on the phonology, lexicon and syntax. The improvements in computational tools led the researchers build and test models that have been ran computer simulations to answer the question. Although the models are mere reflections of the reality, the results have been often useful and insightful. This dissertation follows the same line and proposes a new model, tested in a game based simulation methodology. Besides, this work tries to fill the gap in the studies of lexicon compositionality and proposes a plausible explanation for the transition from single word naming to multi word naming. The direction of the results is in line with the previous research such as the emergence of a stable and communicative language. Moreover compositionality in lexicon is observed with a very simple bag of words syntax. The parameters influencing the results are analyzed in depth. Even though the model does not meet the standards of the real world, future work hints insightful facts about the transition from single word naming to syntax.